Machining capacity measurement of an industrial product service system for turning process

Machining capacity measurement is of great importance in an industrial product service system for turning processes (TP-iPSS) owing to its roles in TP-iPSS evaluation, accounting, and optimization. In this paper, the concepts of potential machining capacity (MCp), real machining capacity (MCr), and total machining capacity (MCt) are presented. As for a configured TP-iPSS whose lathe is given and determined, MCp is majorly influenced by the lathe's attachments, especially the cutting tool, MCr is majorly correlative with cutting parameters (cutting depth, cutting velocity and feed) and workpiece properties besides parameters of cutting tool. Combining different cutting tool with the TP-iPSS, different MCp and potential machining area (A p) can be obtained, then the measurement models of MCp and A p are established through using multivariate regression analysis, and MCr is calculated with the product of MCp and the ratio of A p to A r (real machining area). In addition, MCt in a time interval can be calculated with the sum of MCr in the time interval. Finally, a case is studied to demonstrate the MCp, MCr, and MCt models and their basic applications to TP-iPSS evaluation, accounting, and optimization.

[1]  Horst Meier,et al.  Leadership - From Technology to Use Operation Fields and Solution Approaches for the Automation of Service Processes of Industrial Product-Service-Systems , 2007 .

[2]  Oksana Mont,et al.  Clarifying the Concept of Product-Service System , 2002 .

[3]  Ezio Manzini,et al.  A strategic design approach to develop sustainable product service systems: examples taken from the ‘environmentally friendly innovation’ Italian prize , 2003 .

[4]  A. K. Balaji,et al.  Towards integration of hybrid models for optimized machining performance in intelligent manufacturing systems , 2003 .

[5]  Seiichi Nakajima,et al.  Introduction to TPM: total productive maintenance , 1988 .

[6]  I. S. Jawahir,et al.  Predicting total machining performance in finish turning using integrated fuzzy-set models of the machinability parameters , 1994 .

[7]  Y. S. Tarng,et al.  Cutting-parameter selection for maximizing production rate or minimizing production cost in multistage turning operations , 2000 .

[8]  Pingyu Jiang,et al.  Using iPSS as a new run-time for service-oriented manufacturing executive systems , 2009, 2009 International Conference on Computers & Industrial Engineering.

[9]  Giacomo Copani,et al.  An Innovative Pattern To Design New Business Models In The Machine Tool Industry , 2008, BASYS.

[10]  S. Chatterjee,et al.  Regression Analysis by Example , 1979 .

[11]  M. Gheorghe,et al.  Models of machine tool efficiency and specific consumed energy , 2003 .

[12]  Shaw Voon Wong,et al.  Development of genetic algorithm-based fuzzy rules design for metal cutting data selection , 2002 .

[13]  Uday S. Dixit,et al.  A neural-network-based methodology for the prediction of surface roughness in a turning process , 2005 .

[14]  T S Baines,et al.  State-of-the-art in product-service systems , 2007 .

[15]  Wang Xian-xiang.  Configuration and operation architecture for product service systems of CNC machine tools , 2009 .

[16]  Ciro A. Rodríguez,et al.  Evaluation and modeling of productivity and dynamic capability in high-speed machining centers , 2007 .

[17]  Pingyu Jiang,et al.  A game-theory approach for job scheduling in networked manufacturing , 2009 .

[18]  Peggy Zwolinski,et al.  A representation of a Product-Service System during the design phase - A case study of a helium liquefier , 2006 .

[19]  I. S. Jawahir,et al.  Development of hybrid predictive models and optimization techniques for machining operations , 2007 .